This paper presents a comprehensive photoplethysmography (PPG) sensing analog front-end model in MATLAB for accelerating the design of personalized healthcare hardware. This model consists of four component modules, 1) the transimpedance amplifier (TIA), 2) the analog-to-digital converter (ADC), 3) the DC offset current cancellation block (DCOC), and 4) the LED driver, along with a system module which interacts with the four component modules for evaluating the system power consumption and signal-to-noise ratio (SNR) metrics. Once the designer inputs the power budget and the SNR target, this model presents the designer with the optimized design parameters, visualized power and noise breakdown plots, and detailed component specifications. Additional variables represent the user variances in the model to evaluate the hardware performance for different user groups. Compared to simulations in Cadence, the proposed model achieves worst-case errors of only 6.9% and 0.4% for estimating power and SNR, respectively.